class_docstr: Deterministic/Degenerate distribution.
  Parameters
  ----------
  value
      Tensor containing the values, of shape `(*batch_shape, *event_shape)`.
  F
code: def log_prob(self, x: Tensor) -> Tensor:
      F = self.F
      value = self.value
      is_both_nan = F.broadcast_logical_and(x != x, value != value)
      is_equal_or_both_nan = F.broadcast_logical_or(
          (x == value), is_both_nan
      )
      return F.log(is_equal_or_both_nan)
  
imports: from typing import Dict, List, Optional, Tuple, Type
  import mxnet
  import numpy
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from .distribution import Distribution, _sample_multiple, getF
  from .distribution_output import DistributionOutput
label_desc: Use image node_id 6 for calling the Deterministic obj's underlying member method code with example usage: obj.log_prob(x) and returns: F